CYSMN DataSet1

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install.packages("data.table")
library(data.table)

install.packages("plotly")
library(plotly)

install.packages("stringr")
library(stringr)

install.packages("moments")
library("moments")

# load libraries

Crawford

library(data.table)
library(plotly)
library(stringr)
library("moments")


#pre <- fread("CYSMN_Pre Survey_MODIFIED.csv")
post <- fread("CYSMN_Post Survey_MODIFIED.csv")

scentB <- post[ which(str_detect(post$Q19, "B")),]
scentA <- post[ which(str_detect(post$Q19, "A")),]

# Begin Plots
Questions <- c("Q1", "Q2", "Q3", "Q4", "Q5")
rosemary <- c(mean(scentA$Q1), mean(scentA$Q2), mean(scentA$Q3), mean(scentA$Q4), mean(scentA$Q5))
lavender <- c(mean(scentB$Q1), mean(scentB$Q2), mean(scentB$Q3), mean(scentA$Q4), mean(scentB$Q5))
data <- data.frame(Questions, rosemary, lavender)

fig <- plot_ly(data, x = ~Questions, y = ~rosemary, type = 'bar', name = 'Rosemary')
fig <- fig %>% add_trace(y = ~lavender, name = 'Lavender')
fig <- fig %>% layout(yaxis = list(title = 'Rating'), barmode = 'group')

fig

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Load Data

rr

data <- fread(_Post Survey_MODIFIED.csv)

Add a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Cmd+Option+I. scriptive Statistics

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#Get Type A Questions

COLUMN STATEMENTS

Q1A <- c(scentA\(Q1) Q13A <- c(scentA\)Q13) Q14A <- c(scentA\(Q14) Q2A <- c(scentA\)Q2) Q3A <- c(scentA\(Q3) Q4A <- c(scentA\)Q4) Q5A <- c(scentA\(Q5) Q6A <- c(scentA\)Q6) Q7A <- c(scentA\(Q7) Q8A <- c(scentA\)Q8) Q9A <- c(scentA\(Q9) Q10A <- c(scentA\)Q10) Q11A <- c(scentA\(Q11) Q12A <- c(scentA\)Q12)

AVERAGE STATEMENTS

avgQ1A <- mean(Q1A) avgQ13A <- mean(Q13A) avgQ14A <- mean(Q14A) avgQ2A <- mean(Q2A) avgQ3A <- mean(Q3A) avgQ4A <- mean(Q4A) avgQ5A <- mean(Q5A) avgQ6A <- mean(Q6A) avgQ7A <- mean(Q7A) avgQ8A <- mean(Q8A) avgQ9A <- mean(Q9A) avgQ10A <- mean(Q10A) avgQ11A <- mean(Q11A) avgQ12A <- mean(Q12A)

PRINT STATEMENTS

stringQ1A <- 1 for Type A average:
print(paste(stringQ1A, avgQ1A))

[1] \Question 1 for Type A average:  5.33333333333333\

rr print(\)

[1] \\

rr stringQ13A <- 2 for Type A average:
print(paste(stringQ13A, avgQ13A))

[1] \Question 2 for Type A average:  7\

rr print(\)

[1] \\

rr stringQ14A <- 3 for Type A average:
print(paste(stringQ14A, avgQ14A))

[1] \Question 3 for Type A average:  3.5\

rr print(\)

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rr stringQ2A <- 4 for Type A average:
print(paste(stringQ2A, avgQ2A))

[1] \Question 4 for Type A average:  5.16666666666667\

rr print(\)

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rr stringQ3A <- 5 for Type A average:
print(paste(stringQ3A, avgQ3A))

[1] \Question 5 for Type A average:  5.33333333333333\

rr print(\)

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rr stringQ4A <- 6 for Type A average:
print(paste(stringQ4A, avgQ4A))

[1] \Question 6 for Type A average:  4.66666666666667\

rr print(\)

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rr stringQ5A <- 7 for Type A average:
print(paste(stringQ5A, avgQ5A))

[1] \Question 7 for Type A average:  1.83333333333333\

rr print(\)

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rr stringQ6A <- 8 for Type A average:
print(paste(stringQ6A, avgQ6A))

[1] \Question 8 for Type A average:  6.16666666666667\

rr print(\)

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rr stringQ7A <- 9 for Type A average:
print(paste(stringQ7A, avgQ7A))

[1] \Question 9 for Type A average:  5.5\

rr print(\)

[1] \\

rr stringQ8A <- 10 for Type A average:
print(paste(stringQ8A, avgQ8A))

[1] \Question 10 for Type A average:  3.83333333333333\

rr print(\)

[1] \\

rr stringQ9A <- 11 for Type A average:
print(paste(stringQ9A, avgQ9A))

[1] \Question 11 for Type A average:  1.16666666666667\

rr print(\)

[1] \\

rr stringQ10A <- 12 for Type A average:
print(paste(stringQ10A, avgQ10A))

[1] \Question 12 for Type A average:  5.83333333333333\

rr print(\)

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rr stringQ11A <- 13 for Type A average:
print(paste(stringQ11A, avgQ11A))

[1] \Question 13 for Type A average:  4\

rr print(\)

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rr stringQ12A <- 14 for Type A average:
print(paste(stringQ12A, avgQ12A))

[1] \Question 14 for Type A average:  4.16666666666667\

rr print(\)

[1] \\

rr

#Get Type B Questions

COLUMN STATEMENTS

Q1B <- c(scentB\(Q1) Q13B <- c(scentB\)Q13) Q14B <- c(scentB\(Q14) Q2B <- c(scentB\)Q2) Q3B <- c(scentB\(Q3) Q4B <- c(scentB\)Q4) Q5B <- c(scentB\(Q5) Q6B <- c(scentB\)Q6) Q7B <- c(scentB\(Q7) Q8B <- c(scentB\)Q8) Q9B <- c(scentB\(Q9) Q10B <- c(scentB\)Q10) Q11B <- c(scentB\(Q11) Q12B <- c(scentB\)Q12)

AVERAGE STATEMENTS

avgQ1B <- mean(Q1B) avgQ13B <- mean(Q13B) avgQ14B <- mean(Q14B) avgQ2B <- mean(Q2B) avgQ3B <- mean(Q3B) avgQ4B <- mean(Q4B) avgQ5B <- mean(Q5B) avgQ6B <- mean(Q6B) avgQ7B <- mean(Q7B) avgQ8B <- mean(Q8B) avgQ9B <- mean(Q9B) avgQ10B <- mean(Q10B) avgQ11B <- mean(Q11B) avgQ12B <- mean(Q12B)

PRINT STATEMENTS

stringQ1B <- 1 for Type B average:
print(paste(stringQ1B, avgQ1B))

[1] \Question 1 for Type B average:  6.16666666666667\

rr print(\)

[1] \\

rr stringQ13B <- 2 for Type B average:
print(paste(stringQ13B, avgQ13B))

[1] \Question 2 for Type B average:  6.83333333333333\

rr print(\)

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rr stringQ14B <- 3 for Type B average:
print(paste(stringQ14B, avgQ14B))

[1] \Question 3 for Type B average:  2.33333333333333\

rr print(\)

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rr stringQ2B <- 4 for Type B average:
print(paste(stringQ2B, avgQ2B))

[1] \Question 4 for Type B average:  5.33333333333333\

rr print(\)

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rr stringQ3B <- 5 for Type B average:
print(paste(stringQ3B, avgQ3B))

[1] \Question 5 for Type B average:  6.16666666666667\

rr print(\)

[1] \\

rr stringQ4B <- 6 for Type B average:
print(paste(stringQ4B, avgQ4B))

[1] \Question 6 for Type B average:  5.83333333333333\

rr print(\)

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rr stringQ5B <- 7 for Type B average:
print(paste(stringQ5B, avgQ5B))

[1] \Question 7 for Type B average:  2.33333333333333\

rr print(\)

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rr stringQ6B <- 8 for Type B average:
print(paste(stringQ6B, avgQ6B))

[1] \Question 8 for Type B average:  5.66666666666667\

rr print(\)

[1] \\

rr stringQ7B <- 9 for Type B average:
print(paste(stringQ7B, avgQ7B))

[1] \Question 9 for Type B average:  5.16666666666667\

rr print(\)

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rr stringQ8B <- 10 for Type B average:
print(paste(stringQ8B, avgQ8B))

[1] \Question 10 for Type B average:  2.66666666666667\

rr print(\)

[1] \\

rr stringQ9B <- 11 for Type B average:
print(paste(stringQ9B, avgQ9B))

[1] \Question 11 for Type B average:  1.5\

rr print(\)

[1] \\

rr stringQ10B <- 12 for Type B average:
print(paste(stringQ10B, avgQ10B))

[1] \Question 12 for Type B average:  6\

rr print(\)

[1] \\

rr stringQ11B <- 13 for Type B average:
print(paste(stringQ11B, avgQ11B))

[1] \Question 13 for Type B average:  3.66666666666667\

rr print(\)

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rr stringQ12B <- 14 for Type B average:
print(paste(stringQ12B, avgQ12B))

[1] \Question 14 for Type B average:  4.66666666666667\

rr print(\)

[1] \\

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Wilcox Testing for Type A

testQ1 <- wilcox.test(Q1B, Q1A, mu=0, alt=.sided, conf.int=T, conf.level=0.95, paired=T, exact=T, correct=T)

testQ1 <- t.test(Q1B, Q1A) print(testQ1)

    Welch Two Sample t-test

data:  Q1B and Q1A
t = 0.92848, df = 5.3567, p-value = 0.3931
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -1.428383  3.095050
sample estimates:
mean of x mean of y 
 6.166667  5.333333 

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New data frame for Type A

new_df_TA <- data.frame(Q1A, Q13A, Q14A, Q2A, Q3A, Q4A, Q5A, Q6A, Q7A, Q8A, Q9A, Q10A, Q11A, Q12A)

Skewed Values for Type A

skew_Q1A <- skewness(new_df_TA\(Q1A) skew_Q13A <- skewness(new_df_TA\)Q13A) skew_Q14A <- skewness(new_df_TA\(Q14A) skew_Q2A <- skewness(new_df_TA\)Q2A) skew_Q3A <- skewness(new_df_TA\(Q3A) skew_Q4A <- skewness(new_df_TA\)Q4A) skew_Q5A <- skewness(new_df_TA\(Q5A) skew_Q6A <- skewness(new_df_TA\)Q6A) skew_Q7A <- skewness(new_df_TA\(Q7A) skew_Q8A <- skewness(new_df_TA\)Q8A) skew_Q9A <- skewness(new_df_TA\(Q9A) skew_Q10A <- skewness(new_df_TA\)Q10A) skew_Q11A <- skewness(new_df_TA\(Q11A) skew_Q12A <- skewness(new_df_TA\)Q12A)

rr

library(datasets) data(iris) library(plotly) # https://plotly.com/r/line-and-scatter/